Simulating a Flexible Robotic System based on Musculoskeletal Modeling

Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

A Whole-Body Pose Taxonomy for Loco-Manipulation Tasks

Exploiting interaction with the environment is a promising and powerful way to enhance stability of humanoid robots and robustness while executing locomotion and manipulation tasks. Recently some works have started to show advances in this direction considering humanoid locomotion with multi-contacts, but to be able to fully develop such abilities in a more autonomous way, we need to first understand and classify the variety of possible poses a humanoid robot can achieve to balance. To this end, we propose the adaptation of a successful idea widely used in the field of robot grasping to the field of humanoid balance with multi-contacts: a whole-body pose taxonomy classifying the set of whole-body robot configurations that use the environment to enhance stability. We have revised criteria of classification used to develop grasping taxonomies, focusing on structuring and simplifying the large number of possible poses the human body can adopt. We propose a taxonomy with 46 poses, containing three main categories, considering number and type of supports as well as possible transitions between poses. The taxonomy induces a classification of motion primitives based on the pose used for support, and a set of rules to store and generate new motions. We present preliminary results that apply known segmentation techniques to motion data from the KIT whole-body motion database. Using motion capture data with multi-contacts, we can identify support poses providing a segmentation that can distinguish between locomotion and manipulation parts of an action.Comment: 8 pages, 7 figures, 1 table with full page figure that appears in landscape page, 2015 IEEE/RSJ International Conference on Intelligent Robots and System

Point-light biological motion perception activates human premotor cortex

Motion cues can be surprisingly powerful in defining objects and events. Specifically, a handful of point-lights attached to the joints of a human actor will evoke a vivid percept of action when the body is in motion. The perception of point-light biological motion activates posterior cortical areas of the brain. On the other hand, observation of others' actions is known to also evoke activity in motor and premotor areas in frontal cortex. In the present study, we investigated whether point-light biological motion animations would lead to activity in frontal cortex as well. We performed a human functional magnetic resonance imaging study on a high-field-strength magnet and used a number of methods to increase signal, as well as cortical surface-based analysis methods. Areas that responded selectively to point-light biological motion were found in lateral and inferior temporal cortex and in inferior frontal cortex. The robust responses we observed in frontal areas indicate that these stimuli can also recruit action observation networks, although they are very simplified and characterize actions by motion cues alone. The finding that even point-light animations evoke activity in frontal regions suggests that the motor system of the observer may be recruited to "fill in" these simplified displays